wehub-resource-sync
268 lines
8.6 KiB
Python
268 lines
8.6 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import itertools
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import unittest
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from functools import partial
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from typing import Any
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import numpy as np
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from program_config import ProgramConfig, TensorConfig
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from trt_layer_auto_scan_test import TrtLayerAutoScanTest
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import paddle.inference as paddle_infer
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class TrtConvertDeformableConvTest(TrtLayerAutoScanTest):
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def is_program_valid(self, program_config: ProgramConfig) -> bool:
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inputs = program_config.inputs
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weights = program_config.weights
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attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
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]
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if (
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inputs['input_data'].shape[1]
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!= weights['filter_data'].shape[1] * attrs[0]['groups']
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):
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return False
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return True
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def sample_program_configs(self):
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def compute_output_size(
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input_size: list[int],
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kernel_sizes: list[int],
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attrs: list[dict[str, Any]],
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):
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strides = attrs[0]['strides']
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paddings = attrs[0]['paddings']
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dilations = attrs[0]['dilations']
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output_size = []
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for i, k, s, p, d in zip(
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input_size, kernel_sizes, strides, paddings, dilations
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):
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k = d * (k - 1) + 1
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output_size.append((i + 2 * p - k) // s + 1)
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return output_size
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def generate_input1(
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batch: int,
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input_size: list[int],
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kernel_sizes: list[int],
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attrs: list[dict[str, Any]],
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):
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return np.random.random([batch, 3, *input_size]).astype(np.float32)
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def generate_offset1(
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batch: int,
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input_size: list[int],
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kernel_sizes: list[int],
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attrs: list[dict[str, Any]],
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):
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output_size = compute_output_size(input_size, kernel_sizes, attrs)
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return np.random.random(
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[batch, 2 * np.prod(kernel_sizes), *output_size]
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).astype(np.float32)
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def generate_mask1(
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batch: int,
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input_size: list[int],
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kernel_sizes: list[int],
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attrs: list[dict[str, Any]],
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):
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output_size = compute_output_size(input_size, kernel_sizes, attrs)
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return np.random.random(
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[batch, np.prod(kernel_sizes), *output_size]
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).astype(np.float32)
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def generate_filter1(
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batch: int,
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input_size: list[int],
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kernel_sizes: list[int],
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attrs: list[dict[str, Any]],
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):
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filter = np.random.random([6, 3, *kernel_sizes])
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filter[0][0][0][0] = 8.8978638e-08
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return filter.astype(np.float32)
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for (
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batch,
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input_size,
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kernel_sizes,
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strides,
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paddings,
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groups,
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dilations,
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) in itertools.product(
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[1],
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[[32, 32]],
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[[3, 3]],
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[[1, 1], [2, 2]],
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[[1, 1], [0, 2]],
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[1],
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[[2, 2]],
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):
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dics = [
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{
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"strides": strides,
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"paddings": paddings,
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"groups": groups,
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"dilations": dilations,
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"deformable_groups": 1,
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"im2col_step": 1,
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}
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]
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ops_config = [
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{
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"op_type": "deformable_conv",
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"op_inputs": {
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"Input": ["input_data"],
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"Offset": ["offset_data"],
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"Mask": ["mask_data"],
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"Filter": ["filter_data"],
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},
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"op_outputs": {"Output": ["output_data"]},
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"op_attrs": dics[0],
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}
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]
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ops = self.generate_op_config(ops_config)
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program_config = ProgramConfig(
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ops=ops,
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weights={
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"filter_data": TensorConfig(
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data_gen=partial(
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generate_filter1,
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batch,
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input_size,
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kernel_sizes,
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dics,
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)
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)
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},
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inputs={
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"input_data": TensorConfig(
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data_gen=partial(
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generate_input1,
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batch,
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input_size,
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kernel_sizes,
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dics,
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)
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),
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"offset_data": TensorConfig(
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data_gen=partial(
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generate_offset1,
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batch,
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input_size,
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kernel_sizes,
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dics,
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)
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),
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"mask_data": TensorConfig(
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data_gen=partial(
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generate_mask1,
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batch,
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input_size,
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kernel_sizes,
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dics,
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)
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),
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},
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outputs=["output_data"],
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)
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yield program_config
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def sample_predictor_configs(
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self, program_config
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) -> tuple[paddle_infer.Config, list[int], float]:
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def generate_dynamic_shape(attrs):
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self.dynamic_shape.min_input_shape = {
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"input_data": [1, 3, 32, 32],
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"offset_data": [1, 18, 14, 14],
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"mask_data": [1, 9, 14, 14],
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}
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self.dynamic_shape.max_input_shape = {
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"input_data": [1, 3, 32, 32],
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"offset_data": [1, 18, 32, 32],
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"mask_data": [1, 9, 32, 32],
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}
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self.dynamic_shape.opt_input_shape = {
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"input_data": [1, 3, 32, 32],
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"offset_data": [1, 18, 14, 16],
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"mask_data": [1, 9, 14, 16],
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}
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def clear_dynamic_shape():
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self.dynamic_shape.min_input_shape = {}
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self.dynamic_shape.max_input_shape = {}
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self.dynamic_shape.opt_input_shape = {}
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def generate_trt_nodes_num(attrs, dynamic_shape):
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# TODO: This is just the example, need to be fixed.
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if len(attrs[0]['paddings']) == 4:
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return 1, 2
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else:
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return 1, 4
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attrs = [
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program_config.ops[i].attrs for i in range(len(program_config.ops))
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]
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# for static_shape
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clear_dynamic_shape()
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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program_config.set_input_type(np.float32)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, False),
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1e-5,
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)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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program_config.set_input_type(np.float16)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, False),
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1e-2,
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)
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generate_dynamic_shape(attrs)
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self.trt_param.precision = paddle_infer.PrecisionType.Float32
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program_config.set_input_type(np.float32)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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(1e-5, 1e-5),
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)
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self.trt_param.precision = paddle_infer.PrecisionType.Half
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program_config.set_input_type(np.float16)
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yield (
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self.create_inference_config(),
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generate_trt_nodes_num(attrs, True),
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(1e-2, 1e-2),
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)
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def test(self):
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self.trt_param.workspace_size = 1 << 28
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self.run_test()
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if __name__ == "__main__":
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unittest.main()
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